Rolling Bearing Fault Vibration Signal Denoising Based on Adaptive Morphological Wavelet Perona–Malik Filter Algorithm
This paper proposes an adaptive Perona–Malik filtering algorithm based on the morphological Haar wavelet, which is used for vibration signal denoising in rolling bearing fault diagnosis with strong noise. First, the morphological Haar wavelet operator is utilized to presmooth the noisy signal, and t...
Saved in:
| Main Authors: | Hao Li, Yifan Tan, Yun Pu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/5575497 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Adaptive Wavelet Threshold Rolling Bearing Fault Vibration Signal Denoising Method
by: JI Jun-qing, et al.
Published: (2021-04-01) -
Adopting the Multiresolution Wavelet Analysis in Radial Basis Functions to Solve the Perona-Malik Equation
by: A. Khatoon Abadi, et al.
Published: (2018-10-01) -
Fault Diagnosis of Rolling Bearing Using Improved Wavelet Threshold Denoising and Fast Spectral Correlation Analysis
by: Shaoning Tian, et al.
Published: (2021-01-01) -
Crack fault diagnosis of vibration exciter rolling bearing based on genetic algorithm–optimized Morlet wavelet filter and empirical mode decomposition
by: Xiaoming Han, et al.
Published: (2022-08-01) -
An Adaptive Signal Denoising Method Based on Reweighted SVD for the Fault Diagnosis of Rolling Bearings
by: Baoxiang Wang, et al.
Published: (2025-04-01)